Enhancing the quality of decoded quantized images

ABSTRACT

A system for image enhancement and, more particularly, a system for enhancing the quality of a quantized image.

BACKGROUND OF THE INVENTION

This invention relates to a system for image enhancement and, moreparticularly, to a system for enhancing the quality of a quantizedimage.

As the state of the art of digital signal technology advances, relatedtechnologies such as digital image processing has experienced acorresponding growth and benefit. For example, the development andproliferation of facsimile communications allows images to be encodedinto digital signals, transmitted over conventional telephone line, anddecoded into a close representation of the original images. Image dataare also digitally encoded for ease of storage, modification, copying,etc. As is common experience with growing technologies, the field ofdigital image processing is also experiencing problems with applicationsin new areas.

Problems in the area of digital image processing relate generally toachieving a balance between acceptable image distortion and bit-depthrepresentations. In order to increase the efficiency and therefore theusefulness of digital image decoding schemes, the coding system mustprovide a coded set of image data that is more efficient to store,transmit, etc., than the original image data and must reproduce adecoded image with some minimum level of quality. However, theconversion of relatively high bit rate image data to lower bit rate datavirtually always entails a loss of image quality.

One straightforward method for digitizing an image is to create anartificial grid over the image and to assign a value to each grid spacerepresenting the color of the original image at that grid space. If thegrids are made small enough and the values represent a large enoughrange of color, then the image may be encoded and decoded with smallimage quality degradation. For example, display screen images are madeup of an array of pixels, i.e., picture elements. On a black and whitescreen, each pixel has a value of one or zero representing the on/offstate of the pixel. In a one-to-one bit-to-pixel coding scheme, eachpixel value is represented as a 1 or as a 0 and the entire screen imageis encoded. The result of the encoding is an array of binary values. Todecode the image, the array values are translated into a screen imagehaving pixels on or off in the same order in which they were originallyencoded.

If the image is comprised of more than two distinct colors, then morethan a 1-bit code must be used to represent the pixel values. Forexample, if four distinct colors are present in the image, a 2-bitbinary code can represent all of the values. If the image includes 256distinct colors, an 8-bit binary code is required to uniquely representeach of the color values. The memory requirements for such codingschemes increase as the number of distinct colors in the imageincreases. However, with high bit-depth representation schemes, thequality of the image that results will be good as long as the digitalimage transmission or recovery from storage is successful.

To reduce the size of the encoded digital image, the bit-depthrepresentation of the image may be reduced in some manner. For example,an image with a bit-depth of 6 bits per pixel requires significantlyless storage capacity and bandwidth for transmission than the same sizedimage with 16 bits per pixel.

Decoded images, constructed by a low bit-depth representation, generallysuffer from the following types of degradations: (a) quasi-constant orslowly varying regions suffer from contouring effects and amplifiedgranular noise, and (b) textured regions lose detail.

Contouring effects, which are the result of spatial variations, in adecoded image are generally fairly obvious to the naked eye. Thecontouring effects that appear in the slowly varying regions are alsocaused by the fact that not all of the variations in the intensity ofthe original image are available for the decoded image. For example, ifa region of the original image included an area having 4 intensitychanges therein, the decoded image might represent the area with only 2intensities. In contrast to the contouring effects, the effect of thegranular noise on the viewed image is often mitigated by the very natureof the textured regions. But it can be both amplified or suppressed dueto quantization, as well as altered in spectral appearance.

Kundu et al., U.S. Pat. No. 5,218,649, disclose an image processingtechnique that enhances images by reducing contouring effects. Theenhancement system identifies the edge and non-edge regions in thedecoded image. Different filters are applied to each of these regionsand then they are combined together. A low pass filter (LPF) is used onthe non-edge regions, and a high-pass enhancer is used on the edgeregions. Kundu et al. teaches that the contour artifacts are mostvisible in the non-edge areas, and the LPF will remove these edges.Unfortunately, problems arise in properly identifying the edge regions(is a steep slope an edge or a non-edge?). In addition, problems arisein setting thresholds in the segmentation process because if thecontours have a high enough amplitude, then they will be classified asedges in the segmentation, and thus not filtered out. Moreover, theimage segmentation requires a switch, or if statement, and two full-sizeimage buffers to store the edge map and smooth map, because the size ofthese regions varies from image to image, all of which is expensive andcomputationally intensive.

Chan, U.S. Pat. No. 5,651,078, discloses a system that reduces thecontouring in images reproduced from compressed video signals. Chanteaches that some contours result from discrete cosine transformation(DCT) compression, which are more commonly called blocking artifacts.Chan observes that this problem is most noticeable in the dark regionsof image and as a result adds noise to certain DCT coefficients when theblock's DC term is low (corresponding to a dark area). The resultingnoise masks the contour artifacts, however, the dark areas becomenoisier as a result. The dark areas thus have a noise similar tofilm-grain-like noise which may be preferable to the blocking artifacts.

What is desirable is a system for reducing contouring effects of animage. Because the system does not necessarily affect the encoding ortransmission processes, it can be readily integrated into establishedsystems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a contrast sensitivity function.

FIG. 2 illustrates an image processing technique.

FIG. 3 illustrates a de-contouring functions.

FIG. 4 illustrates another de-contouring function.

FIG. 5 illustrates yet another de-contouring function.

FIG. 6 illustrates a modified image processing technique.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present inventor observed that the many of the objectionablecontouring effects in images appear in regions of the image that aregenerally free from a significant number of edges or otherwise hightexture. The present inventor similarly observed fewer objectionablecontouring effects in regions of the image that have a significantnumber of edges or otherwise high texture. After consideration of thisdifference in observing objectionable contouring effects, the presentinventor considered that the human visual system has lower sensitivityto these contour effects in the high frequency regions of the image andthe human visual system has higher sensitivity to these contour effectsin the low frequency regions of the image. FIG. 1 depicts the overallspatial response of the human visual system with its underlying visualchannels with the coarse quantization below the threshold. Further, themasking effects by the higher frequency content of the image, which islimited to the channels as shown in FIG. 1, further inhibits thevisibility of the steps in the waveforms in the high frequency regionsof the image.

In order to preserve the quality of the image, the system preferably(albeit not necessary) reduces the effects of the objectionable contourswithout having to add noise to the image in order to hide them. Thus thesystem is suitable for use with images that are otherwise free of imagecapture noise, such as computer graphics and line art with gradients.Moreover, it would be preferable (albeit not necessary) that thetechnique is implemented without decision steps or if statements toachieve computational efficiency. Also, the technique should require notmore than a single buffer of a size less than or equal to the image, andmore preferable a buffer less than 30, 20, or 10 percent of the size ofthe image.

Referring to FIG. 2, an input image 100 is provided with bit-depth P.The bit-depth P is frequently 8-bits for many images. As previouslydescribed, the image with a bit depth of P is normally quantized into2^(P) different values but may exhibit contouring effects when displayedon a display with a different bit depth, such as a 10 bit display. Inorder to reduce those aspects of the image that are likely to exhibitcontouring effects the present inventor came to the realization that theaspects of the image that will create contouring effects should beidentified in a suitable manner. In another case, the image may berepresented as P bits, but actually have quantization artifacts due toless than P bits. An example is a DVD image which is represented at 8bits/color but only has 6 bits/color of real information because of thequantization of P or B-frames, or in the YCbCr to RGB conversion. Inmany cases, such as DVD applications, the bit-depth limitation comesfrom the inaccurate color matrix calculation (e.g., insufficientbit-depth in the registers).

To reduce the false step edges of contouring the image is preferablylow-pass filtered 102. The low-pass filter applied to the image alsoacts to increase the bit depth, since the low pass filter is primarilyan averaging operation (averaging across a neighborhood of pixels), andthe averaging results in a higher precision. Other techniques maylikewise be applied to effectively modify the bit depth of the image. Insome cases, the system may also modify the bit depth by switching thenumber of bits used for processing the image, such as 8 bits to 16 bits.One way of characterizing an increase in the bit depth of an image is tomodify in any manner an image to 2^(N) different levels, where N≠P.Alternatively, the image may already have a bit-depth needed/desired forthe output (P=N), but the image itself may have a limited bit depth of P(P<N) from a previous operation. In this case, the value of P should beknown or otherwise determined. Alternatively, the image may have abit-depth of N, but the image itself may have a limited bit-depth of P(P<N).

The result of the low pass filtering of the image is to modify the imageto achieve a bit depth of N. In most cases the bit depth N is thedesired bit depth in the final image, such as an image having bit depthN to be displayed on a N bit display. The low pass filter should besufficiently wide (in the spatial domain) to reduce most false steps(contouring) due to the bit-depth limit P. It is noted that the low passfilter likewise reduces other desirable image information within theimage, so merely low-pass filtering the image is insufficient. Note thatthe low pass filter also reduces much useful image information, such asby severely blurring the image, so this step is insufficient to properlyrectify the undesirable contouring.

The low pass filter 102 may be implemented as Cartesian-separable1-dimensional filters. The use of a pair of 1-dimensional filters ismore computationally efficient than a 2-dimensional filter. The filterpreferably has a rectangular shape. The size of the low pass filterkernel may be based upon the viewing distance and the pixels per inch ofthe display. For a 90 pixel per inch display viewed at 1000 pixelviewing distance, the kernel size should be more than 11 pixels toremove the visibility of a contour edge, but the present inventordetermined that the kernel size should actually be more than 31 pixelsto remove the low frequency modulation component of the false contour.Accordingly, the kernel size should be based upon the low frequencymodulation component of the false contour, as opposed to merely thevisibility of the contour edge.

The resulting image from the low pass filter is primarily the lowfrequency components of the image. It is likewise to be understood thatany suitable filter may be used, such as one that generally attenuatesthe high frequency components with respect to the low frequencycomponents of the image.

The system may subtract 105 the low pass filtered image 102 from theoriginal bit-depth limited image 100 having false contours. This inessence reduces the low-frequency content of the input image, whileprimarily leaving the high frequency content. However, the subtraction105 also results in another attribute, namely, the high frequencyportion of the remaining image contains the high frequency portion ofthe contour artifacts. The result of this operation may be referred toas the HP component 107. It is also noted that the result of the lowpass filter 102 is that the low frequency portion of the remaining imagefrom low pass filter 102 contains the low frequency portion of thecontour artifacts. Accordingly, the contouring artifacts are separatedin some manner between the low frequency and high frequency componentsof the image. One manner of separating the contouring artifacts is ifthe low pass filter kernel is less than the spacing of the contour, thenthe low pass component is considered to contain no contouringcomponents.

The subtraction process 105 leads to a bit-depth increase of 1 due tothe sign. For example a subtraction of two images with 0 to +255 resultsin an image with a range from −255 to +255. Hence the high passcomponent 107 has N+1 bit depth (this is based upon a source image beingpadded to N bits before the subtraction operation). Padding may includeinserting 0's and/or 1's to the least significant bits that are missingfrom the lower bit depth representations relative to the higher.

It is noted that the output of the system may not need N+1 bits, but itshould be able to carry a sign, and thus have a zero mean level. If onlyN bits are used and one bit is dedicated to the sign, then only N−1 bitsare available for addition to the low pass filter image (in the lastaddition step). In that case some edge sharpness and detail may be lost.

As previously described, the result of subtracting the low pass filteredimage from the original image results in an image that maintains highfrequency false contour information. It has been determined that thehigh frequency false contour information that should be reduced arethose having a low amplitude. Accordingly, the low amplitude informationshould be reduced with respect to the high amplitude information. Toreduce the low amplitude high frequency false contour information acoring function 110 may be applied. The coring function 110 may includea hard-threshold coring function, such as for example, if abs(HP)<b thenHP=0, else HP=HP. This effectively reduces the contours, especially ifthe low pass filter is sufficiently large.

Unfortunately, simply applying a hard-threshold coring function, whileacceptable, resulted in unexpected additional artifacts that appear likeislands of color, and as ringing of step edges. After consideration ofthese unexpected artifacts, it was determined that a transitioned coringfunction will both reduce the low amplitude high frequency false contourinformation, and reduce the additional color islands and ringing ofsteps edges. A modified transitional coring function, may be forexample, as:CVout=sign(CVin)*A[0.5−0.5 cos(α|CVin|)] for |CVin|<M   (1)CVout=CVin for |CVin|>M

CVin is the input code value of the HP image to the coring function,while CVout is the output code value of the coring function. M is themerge point that is where the coring behavior ends (or substantiallyends) and the coring function returns to the identify function (or othersuitable function). A and α are parameters selected to ensure twoconditions at the merge point, M, namely:

-   -   (A) amplitude=M    -   (B) slope=1

The first criterion ensures that the coring function has nodiscontinuity in actual value, and the second ensures that the 1^(st)derivative is continuous. The first criterion keeps the tone scalemonotonic in the HP band, and the second avoids mach band typeartifacts. Thus this coring function could even be applied to the lowpass band without such artifacts. It is noted that the coring functionpreferably has a slope that equals 1 that intersects with the origin ofthe plot as well as no second order discontinuities.

The criteria may be restated as follows:M=A[0.5−0.5 cos(αM)] for the amplitude   (2)1=d/dCVin(A[0.5−0.5 cos(αCVin)])|CVin=M   (3)

An example of the coring functions for the value of M=8 with A=11.3 andα=0.25, is shown in FIG. 3 to illustrate the actual mapping due to theeffects of the quantization to N bits. Curve 50 has a slope equal to 1.The curve 52 is equation (1) with the parameters to achieve a mergepoint at 8. The curve 54 are the actual code values if the HP image isquantized to N bits. The curve 56 is the slope of the scaled cosinefunction in equation (1), before the merge point. It is noted that onlythe positive half of CVin is illustrated.

Another example of the coring function for the value of M=4 with A=5.6and α=0.5 is illustrated in FIG. 4. Another example of the coringfunction for the value of M=16 with A=19.75 and α=0.14 is illustrated inFIG. 5.

Referring again to FIG. 2, the next step is to add the cored image 110and the filtered low pass components 102 together at 112. This operationrestores the low frequency information that was reduced back to theimage. The result of this addition operation is N+1 bits. It is N+1since the range may be larger than the input image (for example if thelow pass component for N bits may be 0 to +255, the high pass componentmay have a range of N+1 (−255 to +255) and the result is −255 to +512).Anything out of the range of N bits is clipped (e.g., out of range 0 to+255 for N=8). It turns out that there are a limited number of pixelsthat fall out of that range, and when they do, they are usually isolatededge pixels. The clipping that occurs as a result is not readily visiblein the final image 114.

The number of gray levels is given by N or P, which are bit-depths, sothe numbers of levels is 2^(N) or 2^(P). However, another embodiment isto not use bit-depth to determine the number of gray levels, but thenumbers of gray levels directly.

In a particular implementation, each of the steps shown in FIG. 2 may beapplied to the entire image in a sequential manner, which may result inthe need for large buffers. However, a more memory efficient techniqueinvolves using as a sliding window, where the computations within thewindow are used to compute the pixel at the center of the window (or insome other position in the window). This reduces the memoryrequirements.

In a typical implementation the bit-depth of the input and output imagesare known in advance. This is typically the case in many applications,such as display an eight bit image on a 10-bit display.

While the implementation illustrated in FIG. 2 effective reduces thecontour artifacts, it was determined that it also removes a significantamount of low amplitude detail. While such a result is satisfactory formost cases because the viewer is unaware of exactly how the image shouldreally appear, it turns out to be particularly detrimental in the facialregion of an image, where there is a higher expectation of theappropriate texture by the typical viewer.

To reduce the loss of low amplitude texture details, the presentinventors came to the realization that one may separate the case wherethe low amplitude information is due to the image texture (which isgenerally isotropic) versus the case when the low amplitude informationis due to a false contour (which is generally non-isotropic, a.k.a.,structured error). Generally, isotropic information (“iso” meaning oneor the same, and “tropic” meaning space or direction) has the sametexture in all directions. For example, clean sand on the beach may beconsidered generally isotropic. For example, generally non-isotropicinformation has different textures in different directions. In manycases, the non-isotopic information has the characteristic of an edge.Accordingly, generally isotropic information has more uniform texturerelative to generally non-isotropic information.

While a plurality of different filters may be used, it is preferable touse a single filter, which is more computationally efficient in manycases. The accumulation of local special activity may be determined in asuitable manner, such as for example, a single sigma type filter or asingle sum of absolute differences (SAD) type filter.

The preferred system uses a version where the lowpass filter isimplemented in two Cartesian separable steps (i.e., a cascade of H and Vsteps.), and the filter width spatially is =31 pixels, with a uniformimpulse response. For many images a filter width of 17 or 25 isacceptable. Typically a width less than 17 does not work exceptionallywell for ppi>90.

Referring to FIG. 6, the system may accumulate local activity over alocal region around a pixel of the image to be cored (examples includestandard deviation, α, over a local 9×9 window, sum of absolutedifferences, SAD, over a similar local window). One may select differentcoring functions based on a local activity index (or continuously adjustcoring function based on a local activity index). Local activity measurecould be taken from the mean of the local window, but is more preferablyaround the zero activity point of the HP image (i.e., 0, unless apedestal offset is used.).

1. A method for modifying an image comprising: (a) receiving an imagehaving a first bit depth; (b) modifying said image to create a firstmodified image resulting in a second bit depth different than said firstbit depth in such a manner that the higher frequency content withrespect to the lower frequency content of said modified image isattenuated; (c) modifying said image to create a second modified imagebased upon said first modified image in such a manner to reduce lowerfrequency content of said image; (d) modifying said second modifiedimage to create a third modified image in such a manner to attenuate lowamplitude content with respect to high amplitude content of said secondmodified image; (e) modifying said third modified image based upon saidfirst modified image to increase the amount of low frequency content ofsaid third modified image.
 2. The method of claim 1 wherein saidreceived image of step (a) is represented by X bit depth.
 3. The methodof claim 2 wherein said first modified image of step (b) is representedby Y bit depth.
 4. The method of claim 3 wherein X>Y.
 5. The method ofclaim 1 wherein said modifying of step (b) is a low pass filter.
 6. Themethod of claim 1 wherein said modifying of step (b) wherein said lowerfrequency content is amplified.
 7. The method of claim 1 wherein saidmodifying of step (b) changes the physical bit depth representation ofthe image.
 8. The method of claim 1 wherein said modifying of step (b)does not change the physical bit depth representation of the image. 9.The method of claim 1 being performed in a manner free from includingconditional statements.
 10. The method of claim 1 being performed in amanner using a buffer smaller than 100 percent of said received image.11. The method of claim 1 being performed in a manner using a buffersmaller than 30 percent of said received image.
 12. The method of claim1 being performed in a manner that is free from adding additional noiseto said image.
 13. The method of claim 1 being performed in a mannerbased upon the human visual system.
 14. The method of claim 1 whereinstep (d) including coring of said image.
 15. The method of claim 14wherein said coring is adaptive.
 16. The method of claim 14 wherein saidcoring attenuates non-isotropic content to a greater extent thanisotropic content.
 17. A method for modifying an image having a firstbit depth to an image having a second bit depth, wherein said second bitdepth is greater than said first bit depth comprising: (a) receiving animage having said first bit depth; (b) processing said image in a mannerincreasing the bit depth of said image to said second bit depth; (c)modifying said image to create a modified image based upon saidincreased bit depth image in such a manner to reduce lower frequencycontent of said image; (d) modifying said modified image to createanother image in such a manner to attenuate low amplitude content withrespect to high amplitude content of said modified image; (e) modifyingsaid another image based upon said increased bit depth image to increasethe amount of low frequency content of said another image.
 18. Themethod of claim 17 wherein said processing includes modifying said imageresulting in a second bit depth different than a first bit depth of saidreceived image in such a manner that the higher frequency content withrespect to the lower frequency content of said modified image isattenuated.
 19. The method of claim 17 wherein said processing of step(b) is a low pass filter.
 20. The method of claim 17 wherein saidprocessing of step (b) changes the physical bit depth representation ofthe image.
 21. The method of claim 17 wherein said modifying of step (b)does not change the physical bit depth representation of the image. 22.The method of claim 17 being performed in a manner free from includingconditional statements.
 23. The method of claim 17 being performed in amanner using a buffer smaller than 100 percent of said received image.24. The method of claim 17 being performed in a manner using a buffersmaller than 30 percent of said received image.
 25. The method of claim17 being performed in a manner that is free from adding additional noiseto said image.
 26. The method of claim 17 being performed in a mannerbased upon the human visual system.
 27. The method of claim 17 whereinsaid image having said first bit depth has been previously quantized toa lesser bit depth.
 28. The method of claim 27 wherein said increasedbit depth is represented within an image using the same number of bitsper pixel of said image as said first bit depth.
 29. The method of claim17 wherein step (d) including coring of said image.
 30. The method ofclaim 17 wherein said coring is adaptive.
 31. The method of claim 17wherein said coring attenuates non-isotropic content to a greater extentthan isotropic content.
 32. A method for modifying an image having afirst bit depth to an image having a second bit depth, wherein saidsecond bit depth is greater than said first bit depth comprising: (a)receiving an image having said first bit depth; (b) processing saidimage using a low pass filter to result in a processed image having atleast a bit depth as large as said second bit depth; (c) modifying saidimage to create a modified image based upon said processed image in sucha manner to reduce lower frequency content of said image; (d) modifyingsaid modified image to create another image in such a manner toattenuate low amplitude content with respect to high amplitude contentof said modified image; (e) modifying said another image based upon saidprocessed image to increase the amount of low frequency content of saidanother image.
 33. A method for modifying an image having a first bitdepth to an image having a second bit depth, wherein said second bitdepth is greater than said first bit depth comprising: (a) receiving animage with said first bit depth having the characteristic thatcontouring results when said image is presented with said second bitdepth; (b) processing said image using a low pass filter to result in aprocessed image having at bit depth at least as great as said second bitdepth; (c) modifying said image to create a modified image based uponsaid processed image in such a manner to reduce lower frequency contentof said image; (d) modifying said modified image to create another imagein such a manner to attenuate low amplitude content with respect to highfrequency content of said modified image; (e) modifying said anotherimage based upon said processed image to increase the amount of lowfrequency content of said another image.
 34. The method of claim 33wherein said processed image has said second bit depth.
 35. The methodof claim 33 wherein said processed image has a bit depth greater thansaid second bit depth.
 36. A method for modifying an image comprising(a) receiving said image; (b) processing said image in a manner toselectively reduce contouring artifacts within low frequency regions ofsaid image while not similarly reducing contouring artifacts within highfrequency regions of said image by attenuating the low amplitude highfrequency content of said image.
 37. A method for modifying an imagecomprising (a) receiving said image; (b) processing said image in amanner to reduce contours by modifying the bit depth of said image andattenuating low amplitude high frequency content of said modified image;(c) wherein said processing is characterized by at least one of thefollowing: (i) said processing includes no conditional statements; (ii)said processing requires a buffer smaller than 100 percent of saidimage; (iii) said processing requires a buffer smaller than 30 percentof said image; (iv) said processing requires a buffer smaller than 20percent of said image; (v) said processing requires a buffer smallerthan 10 percent of said image; (vi) said processing includes a low passfilter; (vii) said processing is free from adding additional nose tosaid image; (viii) said processing is based upon the human visualsystem; (ix) said processing includes modifying said image in such amanner that the higher frequency content with respect to the lowerfrequency content of said image is attenuated.
 38. The method of claim37 wherein said processing is characterized by at least said processingincluding no conditional statements.
 39. The method of claim 37 whereinsaid processing is characterized by at least said processing requiring abuffer smaller than 100 percent of said image.
 40. The method of claim37 wherein said processing is characterized by at least said processingrequiring a buffer smaller than 30 percent of said image.
 41. The methodof claim 37 wherein said processing is characterized by at least saidprocessing requiring a buffer smaller than 20 percent of said image. 42.The method of claim 37 wherein said processing is characterized by atleast said processing requiring a buffer smaller than 10 percent of saidimage.
 43. The method of claim 37 wherein said processing ischaracterized by at least said processing including a low pass filter.44. The method of claim 37 wherein said processing is characterized byat least said processing is free from adding additional nose to saidimage.
 45. The method of claim 37 wherein said processing ischaracterized by at least said processing based upon the human visualsystem.
 46. The method of claim 37 wherein said processing ischaracterized by at least said processing including modifying said imagein such a manner that the higher frequency content with respect to thelower frequency content of said image is attenuated.
 47. A method formodifying an image having a first bit depth to an image having a secondbit depth, wherein said second bit depth is greater than said first bitdepth comprising: (a) receiving an image with said first bit depthhaving the characteristic that contouring results when said image ispresented with said second bit depth; (b) processing said image using alow pass filter to result in a processed image having at bit depth atleast as great as said second bit depth; (c) modifying said image tocreate a modified image based upon subtracting said processed image fromsaid image in such a manner to reduce lower frequency content of saidimage; (d) modifying said modified image to create another image in sucha manner to attenuate low amplitude content of said modified image whilefree from substantially attenuating high amplitude content of saidimage; (e) modifying said another image based upon adding said processedimage to said another image to increase the amount of low frequencycontent of said another image resulting in an image having said secondbit depth.